Drug poisoning is now the leading cause of accidental death among adults in the U.S. and opioid analgesic overdose deaths dramatically increased by over 400% and 265% among women and men, respectively, from 1999-2010. The economic cost of opioid-related poisoning was estimated at $20.4 billion in the United States for 2009. Addressing opioid overdose is consistent with the White House goal of reducing drug-induced morbidity and mortality by 15%. San Francisco, historically a leading city for opioid use and related morbidity, witnessed a dramatic decline in heroin-related mortality after improving access to agonist maintenance services and implementing city-wide distribution of naloxone (the short-acting opioid antagonist used to reverse opioid overdose). However, a surge in opioid analgesic mortality negated much of this mortality benefit. We still understand little about who dies from opioid analgesics since there is no local system for monitoring opioid overdose indicators. Moreover, it is unclear how agonist maintenance services and naloxone distribution correlate with geographic concentration of opioid overdose deaths. To effectively respond to opioid overdose, it is critical to develop methods to identify and monitor overdose trends and evaluate the effectiveness of interventions. We propose a secondary analysis of opioid overdose decedent data collected from the California State Electronic Death Reporting System (EDRS) matched with data from the public substance use treatment record database and chart review of all patients cared for in the safety net healthcare system affiliated with the San Francisco Department of Public Health. We will evaluate annuals trends in opioid overdose deaths for San Francisco, overall and by neighborhood using Poisson regression models form 2010-2012 (aim 1). We will also describe the demographic and clinical characteristics of opioid overdose decedents in San Francisco County using toxicology and medical and substance use treatment records (aim 2). W explores the spatial overlap between opioid overdose decedents, lay naloxone distribution and lay naloxone use (aim 3). In exploratory aims, we will calculate the rate of opioid overdose mortality using US census, National HIV Behavioral Surveillance, and prescription drug monitoring program data for denominators, and conduct ecological analyses to look at the relationship between opioid overdose deaths and neighborhood-level factors. Findings from this study will inform the development of a local surveillance system to track opioid-related outcomes, strengthen the capacity to monitor opioid overdose mortality and enhance interventions to reduce opioid overdose mortality.